Development of an enthalpy and carbon dioxide based demand control ventilation for indoor air quality and energy saving with neural network control

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

An enthalpy and carbon dioxide level based demand control ventilation (EDCV) algorithm has been developed. This takes into account both the indoor occupancy level and the energy content of the fresh air and return air while controlling the fresh air supply. It has been applied under various operating conditions to ensure that the most effective control strategy was used. A back propagation (BP) neural network was used to tune the proportional, integral and differential (PID) parameters in order to obtain a good control performance. Experiments were conducted in a medium-sized lecture theatre to verify the performance of the developed EDCV algorithm in a real application. The results showed that acceptable indoor air quality could be obtained with less energy consumption. Under the optimum experimental conditions, about 15.4% of the total cooling energy was saved. The control performance was found to be good with the PID parameters tuned via the neural network.

Original languageEnglish
Pages (from-to)463-475
Number of pages13
JournalIndoor and Built Environment
Volume13
Issue number6
DOIs
Publication statusPublished - Dec 2004

Keywords

  • CO
  • Demand control ventilation
  • Energy saving
  • Enthalpy
  • Indoor air quality
  • Neural network

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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